11/24/2008 @ 6:00AM

Man Vs. Machine On Wall Street

In the midst of the biggest financial crisis in a generation, a tiny biotech company, called Gene Network Sciences, of Cambridge, Mass., thinks it can make Wall Street smarter. How? Get rid of the humans.

Their idea: Take the supercomputers Gene Network Sciences already uses to help
Pfizer
and
Biogen Idec
invent drugs and use them to help hedge funds trade stocks, bonds,and other assets. “Computers and data are smarter than people,” says Colin Hill, the theoretical physicist who founded GNS and will be chairman of its new trading spinoff, Fina Technologies.

“We believe the economy and the financial system are governed by complex networks, just like the genes that control cells and the neurons that control brains,” says Hill. “And we believe that, using artificial intelligence, we can start to extract that circuitry from raw data.”

Already, computers have replaced many of the guys who run trading desks. Now Fina wants to replace the computer-programming math whizzes with more computers. Perhaps one-tenth of fund managers are “quants”–short for quantitative traders–who run computer algorithms that buy and sell stocks so often they may account for one-third to one-half the trading volume on the New York Stock Exchange.

When it works, it makes fortunes. James Simon founded quant firm Renaissance Technologies in 1982, and is now worth $7.4 billion. David E. Shaw, a computational biologist, founded the quant firm D. E. Shaw in 1988, and is now worth $2.7 billion.

But sometimes, groups of these funds lose money at once–they are all using the same math, and thereby make the same bad bets. Fina thinks it can avert the problem by losing the phalanxes of mathematicians and their market-predicting equations and instead letting computers run the show.

Instead of trying to work out a way to predict the market using a human brain, Finas system would dump all available data into a computer system that takes snapshots of billions of possible predictive configurations. Imagine putting a picture puzzle together in that many different ways and deriving probabilities from all of them.

The idea comes out of systems, or network, biology. Genes and proteins interconnect in a complex web. By drawing these connections, companies hope to invent better drugs. Merck in particular has put technology similar to that used by GNS at the center of its approach.

This computerized approach to biology attracted investors who were, in some cases, quants. Two years ago, Hill was having drinks at an upscale Manhattan bar with an investor and a GNS board member named Thomas Paul, then chief investment officer for
Fortress Investment Group
. For years, they’d been toying with the idea of using GNS’ technology to trade stocks. That night, for some reason, the idea finally stuck.

Paul graduated from MIT in 1993 with bachelor’s and master’s degrees in engineering and computer science, and, like many of his peers, went to Wall Street, working first at
Goldman Sachs
and then at
Deutsche Bank
before starting an $800 million fund at Fortress.

He was prepared for the odd world of quants, he said, by playing on the MIT blackjack team–a different version of the team portrayed in the movie 21, in which a group of students figured out that with investor backing, they could consistently beat the house in Las Vegas by counting cards.

“Blackjack was a nice warm-up,” says Paul. In counting cards, as with most quant funds, the idea is to use a mathematical system to do just a little bit better than chance. On his blackjack team, they won hands about 57% of the time. For a big enough quant fund, 51% would be fine.

But finance is “quite different” he says, because blackjack has unchanging rules and measurable odds.

“If you create a [trading] model that believed the world is as it was from 2001 to 2007, that model may very well fail in 2009,” says Paul. “People will get smarter about realizing that quantative modeling solutions need oversight and need skepticism. I’m sure this last year and a half has destroyed a lot of model-based trading strategies. The ones that have done well are the ones that can adapt to a rapidly changing world.”

Oversight? Skepticism? How would that be gained by taking people out of the process and having computers make the predictions? Because the GNS software can also make other predictions that should be more certain than whether stocks will go up or down. In drug development, this might yield blood tests that can predict early on if an experimental medicine is going to fail. A hedge fund might test prediction of trading volume or price ratios, and thereby kill a flawed algorithm before it lost tons of money.

To see whether the biotech software could really work on Wall Street, Paul tried it on a historical model of gas prices. An existing computational model was able to predict whether gas prices would go up or down about 40% of the time. The GNS model was right 79% of the time.

He and Hill recruited another quant, a former MIT classmate of Paul’s, named Josh Holden, to be Fina’s chief executive. They singed on
Reed Elsevier
to invest in the company. Kevin Brown, a manager of Reed Elsevier’s investment fund, says he’s excited particularly because the technology might also be applied to targeting online advertising to particular groups of customers and detecting fraud on the Internet.

But just because they were able to predict gas prices with surprising accuracy doesn’t mean this biotech company’s Wall Street adventure will pan out. “That’s something I’ve heard from hundreds of quants over the years,” says Andrew Lo, director of the laboratory for financial engineering at the Massachusetts Institute of Technology. “We’re going to see the successes, not the failures.”

“Even legitimate and earnest quants can fool themselves into thinking they’ve found the fountain of youth,” says Lo. “This is a problem that is endemic to all quantitative analysis.” One counter to Lo’s healthy skepticism, he says, is the involvement of Thomas Paul. “He’s a very seasoned veteran. He’s also a very good trader.”

Yet some wonder if having machines this involved is a good thing at all. “The market has become a casino,” Sydney M. Williams, a partner at Monness, Crespi, Hardt & Co., a New York equity research and trading firm, wrote in a memo making the rounds on Wall Street. It’s “a place where an increasing number of players care nothing for the fundamentals of individual companies.”